License Plate Reader

Detect and read the largest license plate from an image using the TrafficEye REST API. Use when the user wants ANPR, ALPR, license plate OCR, number plate re...

Security Scan

The skill's code, instructions, and requested environment variables are consistent with a TrafficEye license-plate OCR helper; nothing indicates hidden or unrelated access.

Purpose & Capability

The skill claims to call TrafficEye's recognition API and the included script builds multipart requests and parses TrafficEye-style responses — this matches the description. Minor inconsistency: the registry lists many TRAFFICEYE_* variables as required, while SKILL.md documents sensible defaults and treats most as optional; however, all listed env vars are relevant to configuring the API call.

Instruction Scope

SKILL.md instructs the agent to accept/resolve a local image path, run the bundled Python helper, and present the selected plate payload. The runtime steps (file existence check, calling recognize_plate.py, interpreting response) are narrowly scoped to the stated purpose and reference only the TrafficEye-related environment variables documented in the SKILL.md.

Install Mechanism

There is no install spec; the skill is instruction-plus-script and relies on a local Python binary. No external packages are automatically downloaded or executed at install time.

Credentials

The primary credential (TRAFFICEYE_API_KEY) is appropriate. Other environment variables (API URL, auth mode, key name, file/request field names, request JSON, timeout) are all relevant to configuring the HTTP request, but the registry marking them all as required contradicts the SKILL.md which documents defaults and optionality — this is a configuration/metadata mismatch rather than evidence of unrelated credential access.

Persistence & Privilege

The skill does not request persistent or elevated platform privileges. always is false and the skill does not modify other skills or global agent settings.

Assessment

This skill appears to do what it says: send a local image to TrafficEye and return the largest detected plate. Before installing, consider: (1) Protect your TRAFFICEYE_API_KEY — treat it like any API secret and don't expose it in public repos. (2) The registry marks several TRAFFICEYE_* vars as required, but SKILL.md documents defaults for most; you can usually only set TRAFFICEYE_API_KEY and rely on defaults. (3) Review the bundled recognize_plate.py (it is included) if you want to verify exactly how requests are formed and where network traffic goes. (4) You can test locally without calling the API using the provided sample_response.json to validate selection logic. If you need stronger assurance, run the script in a sandboxed environment or inspect network traffic when invoking the skill.

TrafficEye License Plate Reader

Use this skill when the user wants to read a license plate from an image with the TrafficEye API.

What This Skill Does

1. Accepts a local image path.
2. Uploads the image to the TrafficEye recognition API.
3. Optionally sends a request form field if TRAFFICEYE_REQUEST_JSON is configured.
4. Parses the API response.
5. Picks the largest detected plate by polygon area.
6. Returns the full selected plate payload to the user, including text, type (country), dimension, scores, occlusion, unreadable, and position.

Expected Input

• A local image file path.
• If the user supplied an attachment instead of a path, first resolve it to a local file path and then run the helper.

Default Runtime Assumptions

• The API endpoint defaults to https://trafficeye.ai/recognition.
• The default request payload is {"tasks":["DETECTION","OCR"],"requestedDetectionTypes":["BOX","PLATE"]}.
• The default API-key transport matches the TrafficEye public API example: header mode with header name apikey.
• Auth and request fields remain configurable in case your deployment differs.

Environment Variables

• TRAFFICEYE_API_KEY: required unless passed explicitly to the helper.
• TRAFFICEYE_API_URL: optional, defaults to https://trafficeye.ai/recognition.
• TRAFFICEYE_API_KEY_MODE: one of header, bearer, form, query. Default: header.
• TRAFFICEYE_API_KEY_NAME: key name for header, form, or query mode. Default: apikey.
• TRAFFICEYE_FILE_FIELD: multipart field for the image. Default: file.
• TRAFFICEYE_REQUEST_FIELD: multipart field for the JSON request. Default: request.
• TRAFFICEYE_REQUEST_JSON: JSON string to include as the request field. By default this is {"tasks":["DETECTION","OCR"],"requestedDetectionTypes":["BOX","PLATE"]}.
• TRAFFICEYE_TIMEOUT_S: optional timeout in seconds. Default: 30.

How To Run

Setup your API key:

export TRAFFICEYE_API_KEY='YOUR_REAL_KEY'

Use the bundled helper:

python3 recognize_plate.py /absolute/path/to/image.jpg

For structured output:

python3 recognize_plate.py /absolute/path/to/image.jpg --format json

If the deployment expects Bearer auth:

TRAFFICEYE_API_KEY_MODE=bearer python3 recognize_plate.py /absolute/path/to/image.jpg

If the deployment needs an explicit request payload:

TRAFFICEYE_REQUEST_JSON='{"requestedDetectionTypes":["PLATE"]}' python3 recognize_plate.py /absolute/path/to/image.jpg --format json

Equivalent to the documented public API example:

curl -X POST \
  -H "Content-Type: multipart/form-data" \
  -H "apikey: YOUR_API_KEY_HERE" \
  -F "file=@image.jpg" \
  -F 'request={"tasks":["DETECTION","OCR"],"requestedDetectionTypes":["BOX","PLATE"]}' \
  https://trafficeye.ai/recognition

Agent Workflow

1. Verify that the image path exists.
2. Run python3 recognize_plate.py <image-path> --format json.
3. Present the full selected plate payload to the user, especially text, type, dimension, occlusion, unreadable, and position.
4. If the API returns no readable text, explain that the largest plate was found but OCR text was missing.
5. If authentication fails, ask the user which auth mode their deployment expects and retry with the matching environment variables.

Offline Validation

You can validate the selection logic without calling the API:

python3 recognize_plate.py --response-json-file examples/sample_response.json --format json

Notes

• The helper intentionally chooses the largest plate by geometric area, not by detection confidence.
• The response parser first checks combinations[].roadUsers[].plates[], then also supports roadUsers[].plates[], top-level plates[], and nested plate payloads discovered recursively.
• The default request and auth header mirror the public example at https://www.trafficeye.ai/api.
• The selected result now includes the original plate payload from the API so country/type and all scores are preserved.